SecA hydrogen deuterium exchange mass spectrometry data

Oliver M. Crook

Abstract

This vignette describes how to analyse a mass-spectrometry based hydrogen deuterium exchange experiment, in particular we focus on empirical Bayes functional models and visualisations. This vignette descibes a case-study for analysing secA protein

Package

hdxstats 1.0

1 Introduction

Ahdash et al. performed HDX-MS on the baceterial sec translocon, a multi-protein complex responsible for translocating diverse proteins across the plasma membrane. Here we analyse this experiment using our functional data analysis approaches

2 loading packges

We will first load the packages required in the analysis of this approach. Please install these packages if you do not have them.

3 Data

The data is stored as a .csv, we need to covert it to an object of class QFeatures. This is performed in a number of steps which we detail below. The data is contained within the package and so can be loaded by specificy the correct path.

secApath <- system.file("extdata", "Project_2_SecA_Cluster_Data.csv",
                        package = "hdxstats")

We can now read in the .csv file and have a quick look at the .csv.

secA <- read.csv(secApath)
head(secA) # have a look

##   Protein Start End Sequence Modification Fragment MaxUptake      MHP State
## 1  RANDOM     4   8    LGGTQ           NA       NA         4 475.2511  SecA
## 2  RANDOM     4   8    LGGTQ           NA       NA         4 475.2511  SecA
## 3  RANDOM     4   8    LGGTQ           NA       NA         4 475.2511  SecA
## 4  RANDOM     4   8    LGGTQ           NA       NA         4 475.2511  SecA
## 5  RANDOM     4   8    LGGTQ           NA       NA         4 475.2511  SecA
## 6  RANDOM     4   8    LGGTQ           NA       NA         4 475.2511  SecA
##   Exposure                File z       RT  Inten   Center
## 1     0.00   291018_SecA_REF_5 1 3.693435 710541 475.5310
## 2     0.00   291018_SecA_REF_4 1 4.077553  13038 476.8321
## 3     0.00   291018_SecA_REF_3 1 3.719365 819312 475.4421
## 4     0.00   291018_SecA_REF_2 1 3.712732 559986 475.4829
## 5     0.00   291018_SecA_REF_1 1 3.677982 527423 475.5147
## 6     0.25 011118_SecA_15sec_1 1 3.724814 506213 475.9036

length(unique(secA$Sequence)) # peptide sequences

## [1] 397

unique(secA$State) # States

## [1] "SecA"           "SecAYEG"        "SecAYEG_ADP"    "SecAYEG_AMPPNP"
## [5] "SecA_ADP"       "SecA_AMPPNP"

Let us have a quick visualisation of some the data so that we can see some of the features

filter(secA, Sequence == unique(secA$Sequence[1]), z == 1) %>%
    ggplot(aes(x = Exposure, y = Center, color = factor(State))) + 
    theme_classic() + geom_point(size = 2) + 
    scale_color_manual(values = brewer.pal(n = 7, name = "Set2")) + 
    labs(color = "experiment", x = "Deuterium Exposure", y = "Peptide Mass")

# Parsing to an object of class QFeatures

Working from a .csv is likely to cause issues downstream. Indeed, we run the risk of accidently changing the data or corrupting the file in some way. Secondly, all .csvs will be formatted slightly different and so making extensible tools for these files will be inefficient. Furthermore, working with a generic class used in other mass-spectrometry fields can speed up analysis and adoption of new methods. We will work the class QFeatures from the QFeatures class as it is a powerful and scalable way to store quantitative mass-spectrometry data.

Firstly, the data is storted in long format rather than wide format. We first switch the data to wide format.

secA <- pivot_wider(data.frame(secA),
                        values_from = "Center",
                        names_from = c("Exposure", "File", "State"),
                        id_cols = c("Sequence", "z"))
head(secA)

## # A tibble: 6 x 107
##   Sequence     z `0_291018_SecA_REF_5_SecA` `0_291018_SecA_RE~ `0_291018_SecA_R~
##   <chr>    <int>                      <dbl>              <dbl>             <dbl>
## 1 LGGTQ        1                       476.               477.              475.
## 2 MLIKL        1                       618.               618.              618.
## 3 LIKLLT       1                       701.               701.              701.
## 4 IKLLT        1                       588.               588.              588.
## 5 IINAME       1                       691.               691.              691.
## 6 AMEPEM       1                       708.               708.              708.
## # ... with 102 more variables: 0_291018_SecA_REF_2_SecA <dbl>,
## #   0_291018_SecA_REF_1_SecA <dbl>, 0.25_011118_SecA_15sec_1_SecA <dbl>,
## #   0.25_011118_SecA_15sec_2_SecA <dbl>, 0.25_011118_SecA_15sec_3_SecA <dbl>,
## #   1_011118_SecA_1min_1_SecA <dbl>, 1_011118_SecA_1min_2_SecA <dbl>,
## #   1_011118_SecA_1min_3_SecA <dbl>, 5_011118_SecA_5min_1_SecA <dbl>,
## #   5_011118_SecA_5min_2_SecA <dbl>, 5_011118_SecA_5min_3_SecA <dbl>,
## #   30.000002_011118_SecA_30min_1_SecA <dbl>, ...

We notice that there are many columns with NAs. The follow code chunk removes these columns.

secA <- secA[, colSums(is.na(secA)) != nrow(secA)]

We also note that the colnames are not very informative. We are going to format in a very specific way so that later functions can automatically infer the design from the column names. We provide in the format X(time)rep(replicate)cond(condition)

colnames(secA[-c(1,2)])

##   [1] "0_291018_SecA_REF_5_SecA"                              
##   [2] "0_291018_SecA_REF_4_SecA"                              
##   [3] "0_291018_SecA_REF_3_SecA"                              
##   [4] "0_291018_SecA_REF_2_SecA"                              
##   [5] "0_291018_SecA_REF_1_SecA"                              
##   [6] "0.25_011118_SecA_15sec_1_SecA"                         
##   [7] "0.25_011118_SecA_15sec_2_SecA"                         
##   [8] "0.25_011118_SecA_15sec_3_SecA"                         
##   [9] "1_011118_SecA_1min_1_SecA"                             
##  [10] "1_011118_SecA_1min_2_SecA"                             
##  [11] "1_011118_SecA_1min_3_SecA"                             
##  [12] "5_011118_SecA_5min_1_SecA"                             
##  [13] "5_011118_SecA_5min_2_SecA"                             
##  [14] "5_011118_SecA_5min_3_SecA"                             
##  [15] "30.000002_011118_SecA_30min_1_SecA"                    
##  [16] "30.000002_011118_SecA_30min_2_SecA"                    
##  [17] "30.000002_011118_SecA_30min_3_SecA"                    
##  [18] "0_011118_SecA_YEG_ref_2_SecAYEG"                       
##  [19] "0_011118_SecA_YEG_ref_3_SecAYEG"                       
##  [20] "0_011118_SecA_YEG_ref_1_SecAYEG"                       
##  [21] "0.25_011118_SecA_YEG_15sec_1_SecAYEG"                  
##  [22] "0.25_011118_SecA_YEG_15sec_2_SecAYEG"                  
##  [23] "0.25_011118_SecA_YEG_15sec_3_SecAYEG"                  
##  [24] "1_011118_SecA_YEG_1min_1_SecAYEG"                      
##  [25] "1_011118_SecA_YEG_1min_2_SecAYEG"                      
##  [26] "1_011118_SecA_YEG_1min_3_SecAYEG"                      
##  [27] "5_011118_SecA_YEG_5min_1_SecAYEG"                      
##  [28] "5_011118_SecA_YEG_5min_2_SecAYEG"                      
##  [29] "5_011118_SecA_YEG_5min_3_SecAYEG"                      
##  [30] "30.000002_011118_SecA_YEG_30min_1_SecAYEG"             
##  [31] "30.000002_011118_SecA_YEG_30min_2_SecAYEG"             
##  [32] "30.000002_011118_SecA_YEG_30min_3_SecAYEG"             
##  [33] "0_011118_SecA_YEG_ref_2_SecAYEG_ADP"                   
##  [34] "0_011118_SecA_YEG_ref_3_SecAYEG_ADP"                   
##  [35] "0_011118_SecA_YEG_ref_1_SecAYEG_ADP"                   
##  [36] "0.25_311018_SecA_YEG_ADP_15sec_3_SecAYEG_ADP"          
##  [37] "0.25_311018_SecA_YEG_ADP_15sec_2_SecAYEG_ADP"          
##  [38] "0.25_311018_SecA_YEG_ADP_15sec_1_SecAYEG_ADP"          
##  [39] "1_311018_SecA_YEG_ADP_1min_3_SecAYEG_ADP"              
##  [40] "1_311018_SecA_YEG_ADP_1min_2_SecAYEG_ADP"              
##  [41] "1_311018_SecA_YEG_ADP_1min_1_SecAYEG_ADP"              
##  [42] "5_311018_SecA_YEG_ADP_5min_3_SecAYEG_ADP"              
##  [43] "5_311018_SecA_YEG_ADP_5min_2_SecAYEG_ADP"              
##  [44] "5_311018_SecA_YEG_ADP_5min_1_SecAYEG_ADP"              
##  [45] "30.000002_311018_SecA_YEG_ADP_30min_3_SecAYEG_ADP"     
##  [46] "30.000002_311018_SecA_YEG_ADP_30min_2_SecAYEG_ADP"     
##  [47] "30.000002_311018_SecA_YEG_ADP_30min_1_SecAYEG_ADP"     
##  [48] "0_011118_SecA_YEG_ref_2_SecAYEG_AMPPNP"                
##  [49] "0_011118_SecA_YEG_ref_3_SecAYEG_AMPPNP"                
##  [50] "0_011118_SecA_YEG_ref_1_SecAYEG_AMPPNP"                
##  [51] "0.25_291018_SecYEGA_AMPPNP_15sec_3_SecAYEG_AMPPNP"     
##  [52] "0.25_291018_SecYEGA_AMPPNP_15sec_2_SecAYEG_AMPPNP"     
##  [53] "0.25_291018_SecYEGA_AMPPNP_15sec_1_SecAYEG_AMPPNP"     
##  [54] "1_291018_SecYEGA_AMPPNP_1min_3_SecAYEG_AMPPNP"         
##  [55] "1_291018_SecYEGA_AMPPNP_1min_2_SecAYEG_AMPPNP"         
##  [56] "1_291018_SecYEGA_AMPPNP_1min_1_SecAYEG_AMPPNP"         
##  [57] "5_301018_SecYEGA_AMPPNP_5min_3_SecAYEG_AMPPNP"         
##  [58] "5_301018_SecYEGA_AMPPNP_5min_2_SecAYEG_AMPPNP"         
##  [59] "5_301018_SecYEGA_AMPPNP_5min_1_SecAYEG_AMPPNP"         
##  [60] "30.000002_301018_SecYEGA_AMPPNP_30min_3_SecAYEG_AMPPNP"
##  [61] "30.000002_301018_SecYEGA_AMPPNP_30min_2_SecAYEG_AMPPNP"
##  [62] "30.000002_301018_SecYEGA_AMPPNP_30min_1_SecAYEG_AMPPNP"
##  [63] "0_291018_SecA_REF_5_SecA_ADP"                          
##  [64] "0_291018_SecA_REF_4_SecA_ADP"                          
##  [65] "0_291018_SecA_REF_3_SecA_ADP"                          
##  [66] "0_291018_SecA_REF_2_SecA_ADP"                          
##  [67] "0_291018_SecA_REF_1_SecA_ADP"                          
##  [68] "0.25_311018_SecA_ADP_15sec_4_SecA_ADP"                 
##  [69] "0.25_311018_SecA_ADP_15sec_3_SecA_ADP"                 
##  [70] "0.25_301018_SecA_ADP_15sec_2_SecA_ADP"                 
##  [71] "1_311018_SecA_ADP_1min_3_SecA_ADP"                     
##  [72] "1_311018_SecA_ADP_1min_2_SecA_ADP"                     
##  [73] "1_311018_SecA_ADP_1min_1_SecA_ADP"                     
##  [74] "5_311018_SecA_ADP_5min_3_SecA_ADP"                     
##  [75] "5_311018_SecA_ADP_5min_2_SecA_ADP"                     
##  [76] "5_311018_SecA_ADP_5min_1_SecA_ADP"                     
##  [77] "30.000002_311018_SecA_ADP_30min_3_SecA_ADP"            
##  [78] "30.000002_311018_SecA_ADP_30min_2_SecA_ADP"            
##  [79] "30.000002_311018_SecA_ADP_30min_1_SecA_ADP"            
##  [80] "0_291018_SecA_REF_5_SecA_AMPPNP"                       
##  [81] "0_291018_SecA_REF_4_SecA_AMPPNP"                       
##  [82] "0_291018_SecA_REF_3_SecA_AMPPNP"                       
##  [83] "0_291018_SecA_REF_2_SecA_AMPPNP"                       
##  [84] "0_291018_SecA_REF_1_SecA_AMPPNP"                       
##  [85] "0.25_291018_SecA_AMPPNP_15sec_3_SecA_AMPPNP"           
##  [86] "0.25_291018_SecA_AMPPNP_15sec_2_SecA_AMPPNP"           
##  [87] "0.25_291018_SecA_AMPPNP_15sec_1_SecA_AMPPNP"           
##  [88] "1_291018_SecA_AMPPNP_1min_3_SecA_AMPPNP"               
##  [89] "1_291018_SecA_AMPPNP_1min_2_SecA_AMPPNP"               
##  [90] "1_291018_SecA_AMPPNP_1min_1_SecA_AMPPNP"               
##  [91] "5_291018_SecA_AMPPNP_5min_3_SecA_AMPPNP"               
##  [92] "5_291018_SecA_AMPPNP_5min_2_SecA_AMPPNP"               
##  [93] "5_291018_SecA_AMPPNP_5min_1_SecA_AMPPNP"               
##  [94] "30.000002_291018_SecA_AMPPNP_30min_3_SecA_AMPPNP"      
##  [95] "30.000002_291018_SecA_AMPPNP_30min_2_SecA_AMPPNP"      
##  [96] "30.000002_291018_SecA_AMPPNP_30min_1_SecA_AMPPNP"      
##  [97] "0_291018_SecA_SecYEG_REF_2_SecAYEG"                    
##  [98] "0_291018_SecA_SecYEG_REF_2_SecAYEG_ADP"                
##  [99] "0_291018_SecA_SecYEG_REF_2_SecAYEG_AMPPNP"             
## [100] "0_291018_SecA_YEG_REF_1_SecAYEG"                       
## [101] "0_291018_SecA_YEG_REF_1_SecAYEG_ADP"                   
## [102] "0_291018_SecA_YEG_REF_1_SecAYEG_AMPPNP"                
## [103] "0_291018_SecA_SecYEG_REF_3_SecAYEG"                    
## [104] "0_291018_SecA_SecYEG_REF_3_SecAYEG_ADP"                
## [105] "0_291018_SecA_SecYEG_REF_3_SecAYEG_AMPPNP"

new.colnames <- gsub("0_", "0rep", paste0("X", colnames(secA)[-c(1,2)]))
new.colnames <- gsub("X0.25_", "X15rep", new.colnames) # convert to seconds too
new.colnames <- gsub("X1_", "X60rep", new.colnames) # convert to seconds too
new.colnames <- gsub("X5_", "X300rep", new.colnames) # convert to seconds too
new.colnames <- gsub("X30.000002_", "X1800rep", new.colnames) # convert to seconds too
new.colnames <- gsub(new.colnames, pattern = "rep.*?SecA_", replacement = "rep")
new.colnames <- gsub(new.colnames, pattern = "rep.*?_[0-9]_", replacement = "repcond")
repnumber <- c(seq.int(5), rep(c(1,2,3), times = 19),
               seq.int(5), rep(c(1,2,3), times = 4),
               seq.int(5), rep(c(1,2,3), times = 4), rep(c(4,5,6), each = 3))
new.colnames <- stringr::str_replace_all(string = new.colnames,
                                pattern = "rep",
                                replacement = paste0("rep", repnumber))
new.colnames

##   [1] "X0rep1condSecA"              "X0rep2condSecA"             
##   [3] "X0rep3condSecA"              "X0rep4condSecA"             
##   [5] "X0rep5condSecA"              "X15rep1condSecA"            
##   [7] "X15rep2condSecA"             "X15rep3condSecA"            
##   [9] "X60rep1condSecA"             "X60rep2condSecA"            
##  [11] "X60rep3condSecA"             "X300rep1condSecA"           
##  [13] "X300rep2condSecA"            "X300rep3condSecA"           
##  [15] "X1800rep1condSecA"           "X1800rep2condSecA"          
##  [17] "X1800rep3condSecA"           "X0rep1condSecAYEG"          
##  [19] "X0rep2condSecAYEG"           "X0rep3condSecAYEG"          
##  [21] "X15rep1condSecAYEG"          "X15rep2condSecAYEG"         
##  [23] "X15rep3condSecAYEG"          "X60rep1condSecAYEG"         
##  [25] "X60rep2condSecAYEG"          "X60rep3condSecAYEG"         
##  [27] "X300rep1condSecAYEG"         "X300rep2condSecAYEG"        
##  [29] "X300rep3condSecAYEG"         "X1800rep1condSecAYEG"       
##  [31] "X1800rep2condSecAYEG"        "X1800rep3condSecAYEG"       
##  [33] "X0rep1condSecAYEG_ADP"       "X0rep2condSecAYEG_ADP"      
##  [35] "X0rep3condSecAYEG_ADP"       "X15rep1condSecAYEG_ADP"     
##  [37] "X15rep2condSecAYEG_ADP"      "X15rep3condSecAYEG_ADP"     
##  [39] "X60rep1condSecAYEG_ADP"      "X60rep2condSecAYEG_ADP"     
##  [41] "X60rep3condSecAYEG_ADP"      "X300rep1condSecAYEG_ADP"    
##  [43] "X300rep2condSecAYEG_ADP"     "X300rep3condSecAYEG_ADP"    
##  [45] "X1800rep1condSecAYEG_ADP"    "X1800rep2condSecAYEG_ADP"   
##  [47] "X1800rep3condSecAYEG_ADP"    "X0rep1condSecAYEG_AMPPNP"   
##  [49] "X0rep2condSecAYEG_AMPPNP"    "X0rep3condSecAYEG_AMPPNP"   
##  [51] "X15rep1condSecAYEG_AMPPNP"   "X15rep2condSecAYEG_AMPPNP"  
##  [53] "X15rep3condSecAYEG_AMPPNP"   "X60rep1condSecAYEG_AMPPNP"  
##  [55] "X60rep2condSecAYEG_AMPPNP"   "X60rep3condSecAYEG_AMPPNP"  
##  [57] "X300rep1condSecAYEG_AMPPNP"  "X300rep2condSecAYEG_AMPPNP" 
##  [59] "X300rep3condSecAYEG_AMPPNP"  "X1800rep1condSecAYEG_AMPPNP"
##  [61] "X1800rep2condSecAYEG_AMPPNP" "X1800rep3condSecAYEG_AMPPNP"
##  [63] "X0rep1condSecA_ADP"          "X0rep2condSecA_ADP"         
##  [65] "X0rep3condSecA_ADP"          "X0rep4condSecA_ADP"         
##  [67] "X0rep5condSecA_ADP"          "X15rep1condSecA_ADP"        
##  [69] "X15rep2condSecA_ADP"         "X15rep3condSecA_ADP"        
##  [71] "X60rep1condSecA_ADP"         "X60rep2condSecA_ADP"        
##  [73] "X60rep3condSecA_ADP"         "X300rep1condSecA_ADP"       
##  [75] "X300rep2condSecA_ADP"        "X300rep3condSecA_ADP"       
##  [77] "X1800rep1condSecA_ADP"       "X1800rep2condSecA_ADP"      
##  [79] "X1800rep3condSecA_ADP"       "X0rep1condSecA_AMPPNP"      
##  [81] "X0rep2condSecA_AMPPNP"       "X0rep3condSecA_AMPPNP"      
##  [83] "X0rep4condSecA_AMPPNP"       "X0rep5condSecA_AMPPNP"      
##  [85] "X15rep1condSecA_AMPPNP"      "X15rep2condSecA_AMPPNP"     
##  [87] "X15rep3condSecA_AMPPNP"      "X60rep1condSecA_AMPPNP"     
##  [89] "X60rep2condSecA_AMPPNP"      "X60rep3condSecA_AMPPNP"     
##  [91] "X300rep1condSecA_AMPPNP"     "X300rep2condSecA_AMPPNP"    
##  [93] "X300rep3condSecA_AMPPNP"     "X1800rep1condSecA_AMPPNP"   
##  [95] "X1800rep2condSecA_AMPPNP"    "X1800rep3condSecA_AMPPNP"   
##  [97] "X0rep4condSecAYEG"           "X0rep4condSecAYEG_ADP"      
##  [99] "X0rep4condSecAYEG_AMPPNP"    "X0rep5condSecAYEG"          
## [101] "X0rep5condSecAYEG_ADP"       "X0rep5condSecAYEG_AMPPNP"   
## [103] "X0rep6condSecAYEG"           "X0rep6condSecAYEG_ADP"      
## [105] "X0rep6condSecAYEG_AMPPNP"

We will now parse the data into an object of class QFeatures, we have provided a function to assist with this in the package. If you want to do this yourself use the readQFeatures function from the QFeatures package.

secAqDF <- parseDeutData(object = DataFrame(secA),
                        design = new.colnames,
                        quantcol = 3:105, sequence = "Sequence", charge = "z")

4 normalisation

We normalise the data to uptake values based on the “undeuterated” mass of the peptide

secA_newdf <- DataFrame(data.frame(assay(secAqDF) - apply(assay(secAqDF), 1, function(x) min(x, na.rm = TRUE))))
secA_newdf <- cbind(DataFrame(secA)[,1:2], secA_newdf)

secAqDF_norm <- parseDeutData(secA_newdf, design = new.colnames, quantcol = 3:105, sequence = "Sequence", charge = "z")

Normalised by exchange amides

secAqDF_norm1 <- normalisehdx(secAqDF_norm,
                             sequence = unique(secA$Sequence),
                             method = "pc")

5 Heatmap visualisations of HDX data

To help us get used to the QFeatures we show how to generate a heatmap of these data from this object:

pheatmap(t(assay(secAqDF_norm1)),
         cluster_rows = FALSE, 
         cluster_cols = FALSE,
         color = brewer.pal(n = 9, name = "BuPu"),
         main = "secA heatmap", 
         fontsize = 14,
         legend_breaks = c(0, 1, 2, 3, 4, 5, 6, max(assay(secAqDF))),
         legend_labels = c("0", "1", "2", "3", "4", "5", "6", "Incorporation"))

# Analysis

To simplify the analysis, we subset to examine the protein secA and seA bound to ADP.

secAqDF_norm1_sub <- secAqDF_norm1[,c(1:17, 63:79)]

6 Functional data analysis of HDX-MS data

The hdxstats package uses an empirical Bayes functional approach to analyse the data. We explain this idea in steps so that we can get an idea of the approach. First we fit the parametric model to the data. This will allow us to explore the HdxStatModel class.

res <- differentialUptakeKinetics(object = secAqDF_norm1_sub, #provide a QFeature object
                                  feature = rownames(secAqDF_norm1_sub)[[1]][5], # which peptide to do we fit
                                  start = list(a = NULL, b = NULL,  d = NULL, p = 1)) # what are the starting parameter guesses

## Warning in differentialUptakeKinetics(object = secAqDF_norm1_sub, feature =
## rownames(secAqDF_norm1_sub)[[1]][5], : NAs introduced by coercion

Here, we see the HdxStatModel class, and that a Functional Model was applied to the data and a total of 2 models were fitted.

res

## Object of class "HdxStatModel"
## Method: Functional Model 
## Fitted 2

The nullmodel and alternative slots of an instance of HdxStatModel provide the underlying fitted models. The method and formula slots provide vital information about what analysis was performed. The vis slot provides a ggplot object so that we can visualise the functional fits.

res@vis

Since this is a ggplot object, we can customise in the usual grammatical ways.

res@vis + scale_color_manual(values = brewer.pal(n = 8, name = "Set2")[-c(1,2)])

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

7 Analysis of secA HDX-MS experiment

We have seen the basic aspects of our functional modelling approach. We now wish to roll out our method across all peptides in the experiment. The fitUptakeKinetics function allows us to apply our modelling approach across all the peptide in the experiment. We need to provide a QFeatures object and the features for which we are fitting the model. The design will be extracted from the column names or you can provide a design yourself. The parameter initilisation should also be provided. Sometimes the model can’t be fit on the kinetics. This is either because there is not enough data or through lack of convergence. An error will be reported in these cases but this should not perturb the user. You may wish to try a few starting values if there excessive models that fail fitting.

res <- fitUptakeKinetics(object = secAqDF_norm1_sub,
                         feature = rownames(secAqDF_norm1_sub)[[1]],
                         start = list(a = NULL, b = NULL,  d = NULL, p = 1))

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "too few data points to fit model"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## [1] "too few data points to fit model"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## Error in nlsModel(formula, mf, start, wts) : 
##   singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

## Warning in max(data$value): no non-missing arguments to max; returning -Inf

## Warning in min(data$value): no non-missing arguments to min; returning Inf

## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion

## [1] "model fit failed, likely exessive missing values"

The code chunk above returns a class HdxStatModels indicating that a number of models for peptide have been fit. This is simply a holder for a list of HdxStatModel instances.

res

## Object of class "HdxStatModels"
## Number of models 204

We can easily examine individual fits by going to the underyling HdxStatModel class:

res@statmodels[[4]]@vis

We now wish to apply statistical analysis to these fitted curves. Our approach is an empirical Bayes testing procedure, which borrows information across peptides to stablise variance estimates. Here, we need to provide the original data that was analysed and the HdxStatModels class. The following code chunk returns an object of class HdxStatRes. This object tell us that statistical analysis was performed using our Functional model.

out <- processFunctional(object = secAqDF_norm1_sub, params = res)

## Warning in pf(q = Fstat, df1 = d1, df2 = d2, lower.tail = FALSE): NaNs produced

## Warning in pf(q = modFstat, df1 = d1, df2 = d2, lower.tail = FALSE): NaNs
## produced

out

## Object of class "HdxStatRes"
## Analysed using Functional model

The main slot of interest is the results slot which returns quantities of interest such as p-values and fdr corrected p-values because of multiple testing. The following is the DataFrame of interest.

out@results

## DataFrame with 204 rows and 8 columns
##                   Fstat.Fstat Fstat.numerator Fstat.denomenator       pvals
##                        <list>          <list>            <list>   <numeric>
## LGGTQ_1               0.39418        0.004996         0.0126744 8.10901e-01
## IINAME_1              3.62553      0.00127486       0.000351634 1.89955e-02
## AMEPEM_1              13.6476      0.00240748       0.000176403 4.02221e-06
## MEPEM_1               21.7177       0.0063718       0.000293391 5.65838e-08
## EKLSDEELKGKTAE_3       6.6861     8.53707e-05       1.27684e-05 9.17601e-04
## ...                       ...             ...               ...         ...
## IFQSIG_1             0.114395       0.0051095         0.0446654    0.975564
## PGMQG_1              0.527581       0.0210774          0.039951    0.717140
## VINPGFAF_1           0.753211      0.00637704        0.00846647    0.570388
## VTGTMFL_1            0.219675        0.216819          0.986999    0.922320
## VYAAQSTHLPLKVNM_3           0               0        -0.0287776         NaN
##                           fdr ebayes.pvals  ebayes.fdr fitcomplete
##                     <numeric>    <numeric>   <numeric>   <integer>
## LGGTQ_1           8.52917e-01  7.97194e-01 8.38499e-01           1
## IINAME_1          2.98922e-02  1.58705e-02 2.53678e-02           2
## AMEPEM_1          1.27579e-05  3.02336e-06 1.04024e-05           3
## MEPEM_1           2.87163e-07  3.84268e-08 1.95016e-07           4
## EKLSDEELKGKTAE_3  1.80848e-03  2.18096e-03 4.02486e-03           5
## ...                       ...          ...         ...         ...
## IFQSIG_1             1.000000     0.971886    1.000000         200
## PGMQG_1              0.762196     0.688158    0.731394         201
## VINPGFAF_1           0.612639     0.534855    0.574474         202
## VTGTMFL_1            0.955260     0.908255    0.945517         203
## VYAAQSTHLPLKVNM_3         NaN          NaN         NaN         204

We can now examine the peptides for which the false discovery rate is less than 0.01

which(out@results$ebayes.fdr < 0.01)

##                AMEPEM_1                 MEPEM_1        EKLSDEELKGKTAE_3 
##                       3                       4                       5 
##       EKLSDEELKGKTAEF_2            RARLEKGEVL_3           RARLEKGEVLE_3 
##                       6                       8                       9 
##         RARLEKGEVLENL_2              MRHFDVQL_2               DVQLLGG_1 
##                      10                      18                      19 
##               LLGGMVL_1              LGGMVLNE_1                 MVLNE_1 
##                      20                      21                      22 
##              NERSIAEM_1         MRTGEGKTLTATL_2          RTGEGKTLTATL_2 
##                      23                      24                      25 
##               ATLPAYL_1                  PAYL_1          NALTGKGVHVVT_2 
##                      26                      27                      29 
##       NALTGKGVHVVTVND_2         AQRDAENNRPLFE_2               FLGLTVG_1 
##                      30                      31                      34 
##                LGLTVG_1   LTVGINLPGMPAPAKREAY_3 TVGINLPGMPAPAKREAYAAD_3 
##                      35                      36                      37 
##    INLPGMPAPAKREAYAAD_2               LPGMPAP_1           ITYGTNNEYGF_1 
##                      38                      39                      41 
##          ITYGTNNEYGFD_2                 NEYGF_1                NEYGFD_1 
##                      42                      43                      44 
##         DYLRDNMAFSPEE_2              NMAFSPEE_1                AFSPEE_1 
##                      45                      48                      49 
##                 VDEVD_1                EVDSIL_1       IDEARTPLIISGPAE_3 
##                      51                      52                      54 
##   IDEARTPLIISGPAEDSSE_2          ARTPLIISGPAE_2      ARTPLIISGPAEDSSE_2 
##                      55                      56                      57 
##               FQGEGHF_2                 GEGHF_1              LLVKEGIM_1 
##                      60                      61                      66 
##            LLVKEGIMDE_2               LVKEGIM_1             LVKEGIMDE_1 
##                      67                      69                      70 
##          LVKEGIMDEGES_2         LVKEGIMDEGESL_2               YSPANIM_1 
##                      71                      72                      75 
##              MHHVTAAL_1            AKEGVQIQNE_2        AKEGVQIQNENQTL_2 
##                      78                      85                      86 
##      AKEGVQIQNENQTLAS_2              IQNENQTL_1               SITFQNY_1 
##                      87                      88                      90 
##                  FQNY_1        YEKLAGMTGTADTE_2       YEKLAGMTGTADTEA_2 
##                      93                      96                      97 
##                 FEFSS_1     VVVPTNRPMIRKDLPDL_4   VVVPTNRPMIRKDLPDLVY_4 
##                      98                     103                     104 
##       VPTNRPMIRKDLPDL_3                 VYMTE_1                VYMTEA_1 
##                     105                     107                     108 
##      IIEDIKERTAKGQPVL_3                  QPVL_1               VGTISIE_1 
##                     109                     110                     111 
##                SELVSN_1                 AAIVA_1          AAIVAQAGYPAA_1 
##                     115                     118                     119 
##            IVAQAGYPAA_1          IVAQAGYPAAVT_1               QAGYPAA_1 
##                     120                     121                     122 
##               VTIATNM_1                IVLGGS_1               IVLGGSW_1 
##                     127                     129                     130 
##             ALENPTAEQ_1                 EAGGL_1                  YLSM_1 
##                     133                     137                     139 
##                 YLSME_1         IEHPWVTKAIANA_2              FDIRKQLL_2 
##                     140                     143                     144 
##             FDIRKQLLE_2            FDIRKQLLEY_2         YDDVANDQRRAIY_2 
##                     145                     146                     147 
##          DDVANDQRRAIY_2               SQRNELL_1                DVSETI_1 
##                     148                     149                     152 
##                IREDVF_1         KATIDAYIPPQSL_2                WDIPGL_1 
##                     153                     154                     158 
##              WDIPGLQE_1            WDIPGLQERL_2                  PGLQ_1 
##                     159                     160                     161 
##          QERLKNDFDLDL_2               ERLKNDF_2           ERLKNDFDLDL_2 
##                     162                     163                     164 
##                RLKNDF_2            RLKNDFDLDL_2                  DLDL_1 
##                     165                     166                     167 
##               LDLPIAE_1                  PIAE_1                PIAEWL_1 
##                     168                     169                     170 
##              WLDKEPEL_1                 HEETL_1               HEETLRE_2 
##                     171                     175                     176 
##            RERILAQSIE_2              RILAQSIE_1                  LQTL_1 
##                     177                     178                     180 
##                QTLDSL_1              WKEHLAAM_1             WKEHLAAMD_2 
##                     181                     183                     184 
##                 AAMDY_1                 FAAML_1                LESLKY_1 
##                     185                     192                     194 
##           ESLKYEVISTL_2                EVISTL_1 
##                     195                     196

Let us visualise some of these examples:

res@statmodels[[24]]@vis + res@statmodels[[25]]@vis

We an use a forest plot to examine the differneces

fp <- forestPlot(params = res@statmodels[[25]], condition = c(1, 2))

We can produce a table to actual numbers. We see that at all 4 timepoints the deuterium difference is negative, though the confidence intervals overlap with 0. Our functional approach is picking up this small but reproducible difference.

knitr::kable(fp$data)

	Estimate	confL	confU	rownames	condition
a	0.8908128	0.8208193	0.9608062	a	1
d	0.0227637	-0.0011466	0.0466741	d	1
p	0.5846715	0.4930051	0.6763380	p	1
b	0.0389411	0.0227561	0.0551262	b	1
a1	0.8908128	0.8208193	0.9608062	a	2
d1	0.0227637	-0.0011466	0.0466741	d	2
p1	0.5846715	0.4930051	0.6763380	p	2
b1	0.0389411	0.0227561	0.0551262	b	2
1	0.0000000	-0.0162251	0.0162251	Timepoint 0	Deuterium Difference
2	0.1003596	0.0779054	0.1228137	Timepoint 15	Deuterium Difference
3	0.1761736	0.1270021	0.2253451	Timepoint 60	Deuterium Difference
4	0.4358939	0.3836491	0.4881387	Timepoint 300	Deuterium Difference
5	0.5759391	0.4704157	0.6814625	Timepoint 1800	Deuterium Difference